In planning a trip, such as an automobile trip, people often attempt to determine the best route to reach their destination. For example, people may wish to find a route with the shortest driving distance, or a route that will get them to their destination in the shortest amount of time. In order to provide drivers with this type of route information, traditional mapping services often calculate routes using preset methods based on known information, such as physical distances between locations and nearby connected roads. Recently, some mapping services have started using real-time traffic data to help improve the accuracy of routing information.
Most mapping systems only generate routes for people to select from once a user has begun their trip, or is soon to begin their trip. As a result, a user may not have information about real-time traffic data within enough time to select a more optimal route before beginning their trip. In addition, most mapping systems are relatively static and “forgetful.” In other words, they only provide a simple function of comparing a current location with a destination at a particular moment (usually real-time), and determining and displaying one or more shortest and/or fastest routes. Because these mapping services do not include information regarding a user's preferences or behaviors, these mapping services may not provide optimal routing information for individual users. This may lead to user dissatisfaction with the mapping services, lost time, and increased fuel consumption.
Accordingly, a need exists for systems and methods for facilitating accurate and user-customizable mapping information, based, in part, on user or evidence-based factors. More specifically, a need exists for systems and methods for trip prediction and route recommendations based on user behavioral histories.